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xuchen
Fairseq-S2T
Commits
81caa4ca
Commit
81caa4ca
authored
Mar 29, 2021
by
xuchen
Browse files
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add the speed perturb for the must-c dataset
parent
6a2f4065
显示空白字符变更
内嵌
并排
正在显示
3 个修改的文件
包含
126 行增加
和
47 行删除
+126
-47
egs/mustc/st/run.sh
+4
-0
examples/speech_to_text/prep_mustc_data.py
+122
-46
fairseq_cli/generate.py
+0
-1
没有找到文件。
egs/mustc/st/run.sh
查看文件 @
81caa4ca
...
@@ -41,6 +41,7 @@ share_dict=1
...
@@ -41,6 +41,7 @@ share_dict=1
org_data_dir
=
/media/data/
${
dataset
}
org_data_dir
=
/media/data/
${
dataset
}
data_dir
=
~/st/data/
${
dataset
}
/st
data_dir
=
~/st/data/
${
dataset
}
/st
data_dir
=
~/st/data/
${
dataset
}
/st_perturb_2
test_subset
=(
tst-COMMON
)
test_subset
=(
tst-COMMON
)
# exp
# exp
...
@@ -104,6 +105,7 @@ if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then
...
@@ -104,6 +105,7 @@ if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then
if
[[
!
-e
${
data_dir
}
/
${
lang
}
]]
;
then
if
[[
!
-e
${
data_dir
}
/
${
lang
}
]]
;
then
mkdir
-p
${
data_dir
}
/
${
lang
}
mkdir
-p
${
data_dir
}
/
${
lang
}
fi
fi
source
audio/bin/activate
cmd
=
"python
${
root_dir
}
/examples/speech_to_text/prep_mustc_data.py
cmd
=
"python
${
root_dir
}
/examples/speech_to_text/prep_mustc_data.py
--data-root
${
org_data_dir
}
--data-root
${
org_data_dir
}
...
@@ -118,6 +120,7 @@ if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then
...
@@ -118,6 +120,7 @@ if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then
cmd
=
"python
${
root_dir
}
/examples/speech_to_text/prep_mustc_data.py
cmd
=
"python
${
root_dir
}
/examples/speech_to_text/prep_mustc_data.py
--data-root
${
org_data_dir
}
--data-root
${
org_data_dir
}
--output-root
${
data_dir
}
--output-root
${
data_dir
}
--speed-perturb
--task st
--task st
--add-src
--add-src
--cmvn-type utterance
--cmvn-type utterance
...
@@ -133,6 +136,7 @@ if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then
...
@@ -133,6 +136,7 @@ if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then
echo
-e
"
\0
33[34mRun command:
\n
${
cmd
}
\0
33[0m"
echo
-e
"
\0
33[34mRun command:
\n
${
cmd
}
\0
33[0m"
[[
$eval
-eq
1
]]
&&
eval
${
cmd
}
[[
$eval
-eq
1
]]
&&
eval
${
cmd
}
deactivate
fi
fi
data_dir
=
${
data_dir
}
/
${
lang
}
data_dir
=
${
data_dir
}
/
${
lang
}
...
...
examples/speech_to_text/prep_mustc_data.py
查看文件 @
81caa4ca
...
@@ -13,6 +13,7 @@ from itertools import groupby
...
@@ -13,6 +13,7 @@ from itertools import groupby
from
tempfile
import
NamedTemporaryFile
from
tempfile
import
NamedTemporaryFile
from
typing
import
Tuple
from
typing
import
Tuple
import
string
import
string
import
pickle
import
numpy
as
np
import
numpy
as
np
import
pandas
as
pd
import
pandas
as
pd
...
@@ -28,7 +29,6 @@ from examples.speech_to_text.data_utils import (
...
@@ -28,7 +29,6 @@ from examples.speech_to_text.data_utils import (
save_df_to_tsv
,
save_df_to_tsv
,
cal_gcmvn_stats
,
cal_gcmvn_stats
,
)
)
from
torch
import
Tensor
from
torch.utils.data
import
Dataset
from
torch.utils.data
import
Dataset
from
tqdm
import
tqdm
from
tqdm
import
tqdm
...
@@ -46,14 +46,14 @@ class MUSTC(Dataset):
...
@@ -46,14 +46,14 @@ class MUSTC(Dataset):
utterance_id
utterance_id
"""
"""
SPLITS
=
[
"
train"
,
"dev"
,
"tst-COMMON"
,
"tst-HE
"
]
SPLITS
=
[
"
dev"
,
"tst-COMMON"
,
"tst-HE"
,
"train
"
]
LANGUAGES
=
[
"de"
,
"es"
,
"fr"
,
"it"
,
"nl"
,
"pt"
,
"ro"
,
"ru"
]
LANGUAGES
=
[
"de"
,
"es"
,
"fr"
,
"it"
,
"nl"
,
"pt"
,
"ro"
,
"ru"
]
def
__init__
(
self
,
root
:
str
,
lang
:
str
,
split
:
str
)
->
None
:
def
__init__
(
self
,
root
:
str
,
lang
:
str
,
split
:
str
,
speed_perturb
:
bool
=
False
)
->
None
:
assert
split
in
self
.
SPLITS
and
lang
in
self
.
LANGUAGES
assert
split
in
self
.
SPLITS
and
lang
in
self
.
LANGUAGES
_root
=
Path
(
root
)
/
f
"en-{lang}"
/
"data"
/
split
_root
=
Path
(
root
)
/
f
"en-{lang}"
/
"data"
/
split
wav_root
,
txt_root
=
_root
/
"wav"
,
_root
/
"txt"
wav_root
,
txt_root
=
_root
/
"wav"
,
_root
/
"txt"
assert
_root
.
is_dir
()
and
wav_root
.
is_dir
()
and
txt_root
.
is_dir
()
assert
_root
.
is_dir
()
and
wav_root
.
is_dir
()
and
txt_root
.
is_dir
()
,
(
_root
,
wav_root
,
txt_root
)
# Load audio segments
# Load audio segments
try
:
try
:
import
yaml
import
yaml
...
@@ -61,6 +61,8 @@ class MUSTC(Dataset):
...
@@ -61,6 +61,8 @@ class MUSTC(Dataset):
print
(
"Please install PyYAML to load the MuST-C YAML files"
)
print
(
"Please install PyYAML to load the MuST-C YAML files"
)
with
open
(
txt_root
/
f
"{split}.yaml"
)
as
f
:
with
open
(
txt_root
/
f
"{split}.yaml"
)
as
f
:
segments
=
yaml
.
load
(
f
,
Loader
=
yaml
.
BaseLoader
)
segments
=
yaml
.
load
(
f
,
Loader
=
yaml
.
BaseLoader
)
self
.
speed_perturb
=
[
0.9
,
1.0
,
1.1
]
if
speed_perturb
and
split
.
startswith
(
"train"
)
else
None
# Load source and target utterances
# Load source and target utterances
for
_lang
in
[
"en"
,
lang
]:
for
_lang
in
[
"en"
,
lang
]:
with
open
(
txt_root
/
f
"{split}.{_lang}"
)
as
f
:
with
open
(
txt_root
/
f
"{split}.{_lang}"
)
as
f
:
...
@@ -72,7 +74,8 @@ class MUSTC(Dataset):
...
@@ -72,7 +74,8 @@ class MUSTC(Dataset):
self
.
data
=
[]
self
.
data
=
[]
for
wav_filename
,
_seg_group
in
groupby
(
segments
,
lambda
x
:
x
[
"wav"
]):
for
wav_filename
,
_seg_group
in
groupby
(
segments
,
lambda
x
:
x
[
"wav"
]):
wav_path
=
wav_root
/
wav_filename
wav_path
=
wav_root
/
wav_filename
sample_rate
=
torchaudio
.
info
(
wav_path
.
as_posix
())[
0
]
.
rate
# sample_rate = torchaudio.info(wav_path.as_posix())[0].rate
sample_rate
=
torchaudio
.
info
(
wav_path
.
as_posix
())
.
sample_rate
seg_group
=
sorted
(
_seg_group
,
key
=
lambda
x
:
x
[
"offset"
])
seg_group
=
sorted
(
_seg_group
,
key
=
lambda
x
:
x
[
"offset"
])
for
i
,
segment
in
enumerate
(
seg_group
):
for
i
,
segment
in
enumerate
(
seg_group
):
offset
=
int
(
float
(
segment
[
"offset"
])
*
sample_rate
)
offset
=
int
(
float
(
segment
[
"offset"
])
*
sample_rate
)
...
@@ -91,10 +94,52 @@ class MUSTC(Dataset):
...
@@ -91,10 +94,52 @@ class MUSTC(Dataset):
)
)
)
)
def
__getitem__
(
self
,
n
:
int
)
->
Tuple
[
Tensor
,
int
,
str
,
str
,
str
,
str
]:
def
__getitem__
(
self
,
n
:
int
):
wav_path
,
offset
,
n_frames
,
sr
,
src_utt
,
tgt_utt
,
spk_id
,
utt_id
=
self
.
data
[
n
]
items
=
[]
if
self
.
speed_perturb
is
None
:
waveform
,
_
=
torchaudio
.
load
(
wav_path
,
frame_offset
=
offset
,
num_frames
=
n_frames
)
items
.
append
([
waveform
,
sr
,
src_utt
,
tgt_utt
,
spk_id
,
utt_id
])
else
:
for
speed
in
self
.
speed_perturb
:
sp_utt_id
=
f
"sp{speed}_"
+
utt_id
if
speed
==
1.0
:
waveform
,
_
=
torchaudio
.
load
(
wav_path
,
frame_offset
=
offset
,
num_frames
=
n_frames
)
else
:
waveform
,
_
=
torchaudio
.
load
(
wav_path
,
frame_offset
=
offset
,
num_frames
=
n_frames
)
effects
=
[
[
"speed"
,
f
"{speed}"
],
[
"rate"
,
f
"{sr}"
]
]
waveform
,
_
=
torchaudio
.
sox_effects
.
apply_effects_tensor
(
waveform
,
sr
,
effects
)
items
.
append
([
waveform
,
sr
,
src_utt
,
tgt_utt
,
spk_id
,
sp_utt_id
])
return
items
def
get_fast
(
self
,
n
:
int
):
wav_path
,
offset
,
n_frames
,
sr
,
src_utt
,
tgt_utt
,
spk_id
,
utt_id
=
self
.
data
[
n
]
wav_path
,
offset
,
n_frames
,
sr
,
src_utt
,
tgt_utt
,
spk_id
,
utt_id
=
self
.
data
[
n
]
waveform
,
_
=
torchaudio
.
load
(
wav_path
,
offset
=
offset
,
num_frames
=
n_frames
)
return
waveform
,
sr
,
src_utt
,
tgt_utt
,
spk_id
,
utt_id
items
=
[]
if
self
.
speed_perturb
is
None
:
items
.
append
([
wav_path
,
sr
,
src_utt
,
tgt_utt
,
spk_id
,
utt_id
])
else
:
for
speed
in
self
.
speed_perturb
:
sp_utt_id
=
f
"sp{speed}_"
+
utt_id
items
.
append
([
wav_path
,
sr
,
src_utt
,
tgt_utt
,
spk_id
,
sp_utt_id
])
return
items
def
get_src_text
(
self
):
src_text
=
[]
for
item
in
self
.
data
:
src_text
.
append
(
item
[
4
])
return
src_text
def
get_tgt_text
(
self
):
tgt_text
=
[]
for
item
in
self
.
data
:
tgt_text
.
append
(
item
[
5
])
return
tgt_text
def
__len__
(
self
)
->
int
:
def
__len__
(
self
)
->
int
:
return
len
(
self
.
data
)
return
len
(
self
.
data
)
...
@@ -116,33 +161,77 @@ def process(args):
...
@@ -116,33 +161,77 @@ def process(args):
feature_root
=
output_root
/
"fbank80"
feature_root
=
output_root
/
"fbank80"
feature_root
.
mkdir
(
exist_ok
=
True
)
feature_root
.
mkdir
(
exist_ok
=
True
)
zip_path
=
output_root
/
"fbank80.zip"
zip_path
=
output_root
/
"fbank80.zip"
if
args
.
overwrite
or
not
Path
.
exists
(
zip_path
):
manifest_dict
=
{}
train_text
=
[]
gen_feature_flag
=
False
if
not
Path
.
exists
(
zip_path
):
gen_feature_flag
=
True
for
split
in
MUSTC
.
SPLITS
:
if
not
Path
.
exists
(
output_root
/
f
"{split}_{args.task}.tsv"
):
gen_feature_flag
=
True
break
if
args
.
overwrite
or
gen_feature_flag
:
for
split
in
MUSTC
.
SPLITS
:
for
split
in
MUSTC
.
SPLITS
:
print
(
f
"Fetching split {split}..."
)
print
(
f
"Fetching split {split}..."
)
dataset
=
MUSTC
(
root
.
as_posix
(),
lang
,
split
)
dataset
=
MUSTC
(
root
.
as_posix
(),
lang
,
split
,
args
.
speed_perturb
)
is_train_split
=
split
.
startswith
(
"train"
)
print
(
"Extracting log mel filter bank features..."
)
print
(
"Extracting log mel filter bank features..."
)
if
split
==
'train'
and
args
.
cmvn_type
==
"global"
:
if
is_train_split
and
args
.
cmvn_type
==
"global"
:
print
(
"And estimating cepstral mean and variance stats..."
)
print
(
"And estimating cepstral mean and variance stats..."
)
gcmvn_feature_list
=
[]
gcmvn_feature_list
=
[]
for
waveform
,
sample_rate
,
_
,
_
,
_
,
utt_id
in
tqdm
(
dataset
):
manifest
=
{
c
:
[]
for
c
in
MANIFEST_COLUMNS
}
features
=
extract_fbank_features
(
waveform
,
sample_rate
)
if
args
.
task
==
"st"
and
args
.
add_src
:
manifest
[
"src_text"
]
=
[]
np
.
save
(
for
items
in
tqdm
(
dataset
):
(
feature_root
/
f
"{utt_id}.npy"
)
.
as_posix
(),
for
item
in
items
:
features
# waveform, sample_rate, _, _, _, utt_id = item
)
waveform
,
sr
,
src_utt
,
tgt_utt
,
speaker_id
,
utt_id
=
item
features_path
=
(
feature_root
/
f
"{utt_id}.npy"
)
.
as_posix
()
features
=
extract_fbank_features
(
waveform
,
sr
,
Path
(
features_path
))
# np.save(
# (feature_root / f"{utt_id}.npy").as_posix(),
# features
# )
manifest
[
"id"
]
.
append
(
utt_id
)
duration_ms
=
int
(
waveform
.
size
(
1
)
/
sr
*
1000
)
# duration_ms = int(time_dict[utt_id] / sr * 1000)
manifest
[
"n_frames"
]
.
append
(
int
(
1
+
(
duration_ms
-
25
)
/
10
))
if
args
.
lowercase_src
:
src_utt
=
src_utt
.
lower
()
if
args
.
rm_punc_src
:
for
w
in
string
.
punctuation
:
src_utt
=
src_utt
.
replace
(
w
,
""
)
manifest
[
"tgt_text"
]
.
append
(
src_utt
if
args
.
task
==
"asr"
else
tgt_utt
)
if
args
.
task
==
"st"
and
args
.
add_src
:
manifest
[
"src_text"
]
.
append
(
src_utt
)
manifest
[
"speaker"
]
.
append
(
speaker_id
)
if
split
==
'train'
and
args
.
cmvn_type
==
"global"
:
if
split
==
'train'
and
args
.
cmvn_type
==
"global"
and
not
utt_id
.
startswith
(
"sp"
)
:
if
len
(
gcmvn_feature_list
)
<
args
.
gcmvn_max_num
:
if
len
(
gcmvn_feature_list
)
<
args
.
gcmvn_max_num
:
gcmvn_feature_list
.
append
(
features
)
gcmvn_feature_list
.
append
(
features
)
if
split
==
'train'
and
args
.
cmvn_type
==
"global"
:
if
is_train_split
and
args
.
size
!=
-
1
and
len
(
manifest
[
"id"
])
>
args
.
size
:
break
if
is_train_split
:
if
args
.
task
==
"st"
and
args
.
add_src
and
args
.
share
:
train_text
.
extend
(
list
(
set
(
tuple
(
manifest
[
"src_text"
]))))
train_text
.
extend
(
dataset
.
get_tgt_text
())
if
is_train_split
and
args
.
cmvn_type
==
"global"
:
# Estimate and save cmv
# Estimate and save cmv
stats
=
cal_gcmvn_stats
(
gcmvn_feature_list
)
stats
=
cal_gcmvn_stats
(
gcmvn_feature_list
)
with
open
(
output_root
/
"gcmvn.npz"
,
"wb"
)
as
f
:
with
open
(
output_root
/
"gcmvn.npz"
,
"wb"
)
as
f
:
np
.
savez
(
f
,
mean
=
stats
[
"mean"
],
std
=
stats
[
"std"
])
np
.
savez
(
f
,
mean
=
stats
[
"mean"
],
std
=
stats
[
"std"
])
manifest_dict
[
split
]
=
manifest
# Pack features into ZIP
# Pack features into ZIP
print
(
"ZIPing features..."
)
print
(
"ZIPing features..."
)
create_zip
(
feature_root
,
zip_path
)
create_zip
(
feature_root
,
zip_path
)
...
@@ -159,33 +248,13 @@ def process(args):
...
@@ -159,33 +248,13 @@ def process(args):
zip_manifest
=
get_zip_manifest
(
zip_path
)
zip_manifest
=
get_zip_manifest
(
zip_path
)
# Generate TSV manifest
# Generate TSV manifest
print
(
"Generating manifest..."
)
print
(
"Generating manifest..."
)
for
split
in
MUSTC
.
SPLITS
:
for
split
,
manifest
in
manifest_dict
.
items
():
is_train_split
=
split
.
startswith
(
"train"
)
is_train_split
=
split
.
startswith
(
"train"
)
manifest
=
{
c
:
[]
for
c
in
MANIFEST_COLUMNS
}
if
args
.
task
==
"st"
and
args
.
add_src
:
for
utt_id
in
manifest
[
"id"
]:
manifest
[
"src_text"
]
=
[]
dataset
=
MUSTC
(
args
.
data_root
,
lang
,
split
)
for
wav
,
sr
,
src_utt
,
tgt_utt
,
speaker_id
,
utt_id
in
tqdm
(
dataset
):
manifest
[
"id"
]
.
append
(
utt_id
)
manifest
[
"audio"
]
.
append
(
zip_manifest
[
utt_id
])
manifest
[
"audio"
]
.
append
(
zip_manifest
[
utt_id
])
duration_ms
=
int
(
wav
.
size
(
1
)
/
sr
*
1000
)
manifest
[
"n_frames"
]
.
append
(
int
(
1
+
(
duration_ms
-
25
)
/
10
))
if
args
.
lowercase_src
:
src_utt
=
src_utt
.
lower
()
if
args
.
rm_punc_src
:
for
w
in
string
.
punctuation
:
src_utt
=
src_utt
.
replace
(
w
,
""
)
manifest
[
"tgt_text"
]
.
append
(
src_utt
if
args
.
task
==
"asr"
else
tgt_utt
)
if
args
.
task
==
"st"
and
args
.
add_src
:
manifest
[
"src_text"
]
.
append
(
src_utt
)
manifest
[
"speaker"
]
.
append
(
speaker_id
)
if
is_train_split
and
args
.
size
!=
-
1
and
len
(
manifest
[
"id"
])
>
args
.
size
:
break
if
is_train_split
:
if
args
.
task
==
"st"
and
args
.
add_src
and
args
.
share
:
train_text
.
extend
(
manifest
[
"src_text"
])
train_text
.
extend
(
manifest
[
"tgt_text"
])
df
=
pd
.
DataFrame
.
from_dict
(
manifest
)
df
=
pd
.
DataFrame
.
from_dict
(
manifest
)
df
=
filter_manifest_df
(
df
,
is_train_split
=
is_train_split
)
df
=
filter_manifest_df
(
df
,
is_train_split
=
is_train_split
)
save_df_to_tsv
(
df
,
output_root
/
f
"{split}_{args.task}.tsv"
)
save_df_to_tsv
(
df
,
output_root
/
f
"{split}_{args.task}.tsv"
)
...
@@ -207,7 +276,9 @@ def process(args):
...
@@ -207,7 +276,9 @@ def process(args):
for
split
in
MUSTC
.
SPLITS
:
for
split
in
MUSTC
.
SPLITS
:
if
split
.
startswith
(
"train"
):
if
split
.
startswith
(
"train"
):
dataset
=
MUSTC
(
args
.
data_root
,
lang
,
split
)
dataset
=
MUSTC
(
args
.
data_root
,
lang
,
split
)
for
wav
,
sr
,
src_utt
,
tgt_utt
,
speaker_id
,
utt_id
in
dataset
:
src_text
=
dataset
.
get_src_text
()
tgt_text
=
dataset
.
get_tgt_text
()
for
src_utt
,
tgt_utt
in
zip
(
src_text
,
tgt_text
):
if
args
.
task
==
"st"
and
args
.
add_src
and
args
.
share
:
if
args
.
task
==
"st"
and
args
.
add_src
and
args
.
share
:
if
args
.
lowercase_src
:
if
args
.
lowercase_src
:
src_utt
=
src_utt
.
lower
()
src_utt
=
src_utt
.
lower
()
...
@@ -215,6 +286,7 @@ def process(args):
...
@@ -215,6 +286,7 @@ def process(args):
src_utt
=
src_utt
.
translate
(
None
,
string
.
punctuation
)
src_utt
=
src_utt
.
translate
(
None
,
string
.
punctuation
)
train_text
.
append
(
src_utt
)
train_text
.
append
(
src_utt
)
train_text
.
append
(
tgt_utt
)
train_text
.
append
(
tgt_utt
)
with
NamedTemporaryFile
(
mode
=
"w"
)
as
f
:
with
NamedTemporaryFile
(
mode
=
"w"
)
as
f
:
for
t
in
train_text
:
for
t
in
train_text
:
f
.
write
(
t
+
"
\n
"
)
f
.
write
(
t
+
"
\n
"
)
...
@@ -242,8 +314,9 @@ def process(args):
...
@@ -242,8 +314,9 @@ def process(args):
asr_spm_filename
=
asr_spm_filename
,
asr_spm_filename
=
asr_spm_filename
,
share_src_and_tgt
=
True
if
args
.
task
==
"asr"
else
False
share_src_and_tgt
=
True
if
args
.
task
==
"asr"
else
False
)
)
# Clean up
# Clean up
shutil
.
rmtree
(
feature_root
)
#
shutil.rmtree(feature_root)
def
process_joint
(
args
):
def
process_joint
(
args
):
...
@@ -305,8 +378,11 @@ def main():
...
@@ -305,8 +378,11 @@ def main():
parser
.
add_argument
(
"--vocab-size"
,
default
=
8000
,
type
=
int
)
parser
.
add_argument
(
"--vocab-size"
,
default
=
8000
,
type
=
int
)
parser
.
add_argument
(
"--task"
,
type
=
str
,
choices
=
[
"asr"
,
"st"
])
parser
.
add_argument
(
"--task"
,
type
=
str
,
choices
=
[
"asr"
,
"st"
])
parser
.
add_argument
(
"--size"
,
default
=-
1
,
type
=
int
)
parser
.
add_argument
(
"--size"
,
default
=-
1
,
type
=
int
)
parser
.
add_argument
(
"--speed-perturb"
,
action
=
"store_true"
,
default
=
False
,
help
=
"apply speed perturbation on wave file"
)
parser
.
add_argument
(
"--joint"
,
action
=
"store_true"
,
help
=
""
)
parser
.
add_argument
(
"--joint"
,
action
=
"store_true"
,
help
=
""
)
parser
.
add_argument
(
"--share"
,
action
=
"store_true"
,
help
=
"share the transcription and translation"
)
parser
.
add_argument
(
"--share"
,
action
=
"store_true"
,
help
=
"share the tokenizer and dictionary of the transcription and translation"
)
parser
.
add_argument
(
"--add-src"
,
action
=
"store_true"
,
help
=
"add the src text for st task"
)
parser
.
add_argument
(
"--add-src"
,
action
=
"store_true"
,
help
=
"add the src text for st task"
)
parser
.
add_argument
(
"--asr-prefix"
,
type
=
str
,
help
=
"prefix of the asr dict"
)
parser
.
add_argument
(
"--asr-prefix"
,
type
=
str
,
help
=
"prefix of the asr dict"
)
parser
.
add_argument
(
"--lowercase-src"
,
action
=
"store_true"
,
help
=
"lowercase the source text"
)
parser
.
add_argument
(
"--lowercase-src"
,
action
=
"store_true"
,
help
=
"lowercase the source text"
)
...
...
fairseq_cli/generate.py
查看文件 @
81caa4ca
...
@@ -81,7 +81,6 @@ def _main(cfg: DictConfig, output_file):
...
@@ -81,7 +81,6 @@ def _main(cfg: DictConfig, output_file):
# Load dataset splits
# Load dataset splits
task
=
tasks
.
setup_task
(
cfg
.
task
)
task
=
tasks
.
setup_task
(
cfg
.
task
)
# Set dictionaries
# Set dictionaries
try
:
try
:
src_dict
=
getattr
(
task
,
"source_dictionary"
,
None
)
src_dict
=
getattr
(
task
,
"source_dictionary"
,
None
)
...
...
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